def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 224, 224
num_classes = 1000
train_inputs = random_ops.random_uniform(
(train_batch_size, height, width, 3))
inception_v1.inception_v1(train_inputs, num_classes)
eval_inputs = random_ops.random_uniform(
(eval_batch_size, height, width, 3))
logits, _ = inception_v1.inception_v1(eval_inputs,
num_classes,
reuse=True)
predictions = math_ops.argmax(logits, 1)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (eval_batch_size, ))
def testLogitsNotSqueezed(self):
num_classes = 25
images = random_ops.random_uniform([1, 224, 224, 3])
logits, _ = inception_v1.inception_v1(images,
num_classes=num_classes,
spatial_squeeze=False)
with self.cached_session() as sess:
variables.global_variables_initializer().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape),
[1, 1, 1, num_classes])
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, end_points = inception_v1.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = inception_v1.inception_v1(eval_inputs,
num_classes,
is_training=False)
predictions = math_ops.argmax(logits, 1)
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(predictions)
self.assertEqual(output.shape, (batch_size, ))
def testUnknownBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = array_ops.placeholder(dtypes.float32,
(None, height, width, 3))
logits, _ = inception_v1.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
images = random_ops.random_uniform((batch_size, height, width, 3))
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEqual(output.shape, (batch_size, num_classes))
def testUnknownImageShape(self):
ops.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.cached_session() as sess:
inputs = array_ops.placeholder(dtypes.float32,
shape=(batch_size, None, None, 3))
logits, end_points = inception_v1.inception_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_5c']
feed_dict = {inputs: input_np}
variables.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape),
[batch_size, 7, 7, 1024])